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EPSegNet: Lightweight Semantic Recalibration and Assembly for Efficient Polyp Segmentation

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Wu, Huisi Zhao, Zebin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

Colorectal cancer (CRC) is among the most common malignancies and the detection and removal of polyps at the early stage is of great importance to prevent it. However, current state-of-the-art high-accuracy methods for polyps segmentation have a large number of parameters and a stringent requirement for computational cost, while lightweight and fast models significantly sacrifice accuracy. Currently, medical semantic segmentation algorithms are mostly based on encoder-decoder architecture. Pixelwise spatial information has been proven to be very important to the quality of features extracted by encoders. However, almost all existing approaches capturing it suffer from high computational complexity. Furthermore, the capacity of the traditional decoder is limited by its limited receptive fields. To comprehensively address the above problems, we propose a novel efficient polyp segmentation network (EPSegNet) to simultaneously fulfill the requirements of accuracy, size, and speed. First, we propose a lightweight feature extraction and recalibration module (LFERM), which can efficiently extract dense multiscale features. Specifically, in LFERM, we propose a spatial information recalibration (SIR) block for efficiently refining spatial information. Based on LFERMs, we develop an encoder. Moreover, we propose a novel lightweight semantic assembly decoder (LSAD) that assembles both channelwise and pixelwise semantics from a global context view. Finally, we combine the encoder and LSAD to form the proposed EPSegNet. Experiments on Kvasir-SEG, CVC-ClinicDB, and CVC-ColonDB datasets demonstrate that the proposed EPSegNet achieves the best balance between accuracy and size among state-of-the-art models and obtains a fast speed for polyp segmentation. Without any pretraining and postprocessing, our method achieves 79.37% intersection over union (IoU) and 86.74% Dice on the Kvasie-SEG dataset with only 0.34 million parameters and a speed of 128 frames/s (FPS) at the input size of

关键词： Feature extraction Semantics Accuracy Decoding Shape Data mining Semantic segmentation Colonoscopy Context modeling Computational modeling encoder-decoder lightweight polyp segmentation semantic assembly semantic recalibration

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MMAN-M2: Multiple multi-head attentions network based on encoder with missing modalities

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PATTERN RECOGNITION LETTERS 2024年 177卷 110-120页

作者： Li, Jiayao Li, Li Sun, Ruizhi Yuan, Gang Wang, Shufan Sun, Shulin China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Beijing Informat Sci & Technol Univ Comp Sch Beijing 100101 Peoples R China Minist Agr Sci Res Base Integrated Technol Precis Agr Anim Hu Beijing 100083 Peoples R China

Multi-modal fusion is a hot topic in field of multi-modal learning. Most of the previous multi-modal fusion tasks are based on the complete modality. Existing researches on missing multi-modal fusion fail to consider the random missing of modalities, thereby lacking robustness. And most of methods are based on the correlation between missing and non-missing modalities, ignoring missing modalities contextual information. Considering the above two issues, we designed a multiple multi-head attentions network based on encoder with missing modalities (MMAN-M2). Firstly, the multi-head attention network is used to represent the single modality by extracting potential features based on the entire sequence, and then they are fused;Then, the missing modality context features are extracted by optimizing the result of multi-modal fusion including missing and non-missing features data, and the missing modalities are encoded through the encoding module;Finally, the Transformer encoder-decoder module is used to train the network model by mapping obtaining global information to multiple spaces and integrating our uncertain multi-modal encoding, and it realizes the classification of multi-modal fusion for evaluating model performance. Extensive experiments on multi-modal public datasets show that the proposed method has the best effect and can effectively improve the classification performance of multi-modal fusion.

关键词： Multi-modal fusion Multi-head attention Random missing modalities encoder-decoder Missing modalities

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Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder

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JOURNAL OF HYDROLOGY 2024年 629卷

作者： Jahangir, Mohammad Sina Quilty, John Univ Waterloo Dept Civil & Environm Engn Waterloo ON Canada

Probabilistic hydrological forecasting has gained increasing importance in recent years, as it offers essential information for risk-based decision-making and flood management. Traditional hydrological models often produce deterministic forecasts, which do not account for the inherent uncertainties in hydrological systems. Although many previous studies have investigated using deep learning (DL) models for hydrological prediction, the development of probabilistic DL models (especially, generative models) has not yet been thoroughly examined for hydrological forecasting. The present study investigates the efficacy of a generative DL model, namely, conditional variational auto-encoder (CVAE). CVAE is applied for one-seven day(s) ahead probabilistic streamflow forecasting in 75 basins from the Canadian model parameter experiment (CANOPEX) database. The CVAE forecast model, which outputs forecasts in the form of a probability distribution, was benchmarked against two state-of-the-art quantile-based DL models: quantile-based encoder-decoder (ED) and quantile-based CVAE (QCVAE). The latter outputs forecasts for specific quantiles of a probability distribution (here, q = 0.05, 0.5, 0.95). More than 9000 models were developed based on different basins, input variable sets, and model structures. The models were evaluated in terms of point forecast accuracy and forecast reliability. The results indicate that the CVAE model generally outperforms the benchmark models in terms of reliability at a 90 % confidence level (median reliability of 92.49 % compared to 87.35 % and 84.59 % for ED and QCVAE, respectively). However, the quantile-based forecast models produce slightly more accurate point forecasts than the CVAE (median Kling-Gupta efficiency (KGE) of 0.88 compared to 0.90 for both ED and QCVAE). Notably, the CVAE model exhibits superior probabilistic forecasts in basins with poor point forecast accuracy, highlighting its usefulness over benchmark methods in difficult-to-fore

关键词： Conditional Variational Auto-encoder encoder-decoder Deep Learning Streamflow Forecasting LSTM

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Detecting log anomaly using subword attention encoder and probabilistic feature selection

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APPLIED INTELLIGENCE 2023年第19期53卷 22297-22312页

作者： Hariharan, M. Mishra, Abhinesh Ravi, Sriram Sharma, Ankita Tanwar, Anshul Sundaresan, Krishna Ganesan, Prasanna Karthik, R. Cisco Syst India Pvt Ltd Bengaluru India Vellore Inst Technol Ctr Cyber Phys Syst Chennai India

Log anomaly is a manifestation of a software system error or security threat. Detecting such unusual behaviours across logs in real-time is the driving force behind large-scale autonomous monitoring technology that can rapidly alert zero-day attacks. Increasingly, AI methods are being used to process voluminous log datasets and reveal patterns of correlated anomaly. In this paper, we propose an enhanced approach to learning semantic-aware embeddings for logs called the Subword encoder Neural network (SEN). Solving upon a key limitation of previous semantic log parsing works, the proposed work introduces the concept of learning word vectors from subword-level granularity using an attention encoder strategy. The learnt embeddings reflect the contextual/lexical relationships at the word level. As a result, the learnt word representations precisely capture new log messages previously not seen by the model. Furthermore, we develop a novel feature distillation algorithm termed Naive Bayes Feature Selector (NBFS) to extract useful log events. This probabilistic technique examines the occurrence pattern of events to only select the salient ones that can aid anomaly detection. To our best knowledge, this is the first attempt to associate affinity to log events based on the target task. Since the predictions can be traced to the log messages, the AI is inherently explainable too. The model outperforms state-of-the-art methods by a fair margin. It achieves a 0.99 detection F1-score on the benchmarked BGL, HDFS and OpenStack log datasets.

关键词： Deep learning Self-attention Naive Bayes Syslog Anomaly detection encoder-decoder

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N-Net: an UNet architecture with dual encoder for medical image segmentation

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第6期17卷 3073-3081页

作者： Liang, Bingtao Tang, Chen Zhang, Wei Xu, Min Wu, Tianbo Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Tianjin Med Univ Tianjin Eye Hosp Tianjin Eye Inst Tianjin Key Lab Ophthalmol & Visual SciClin Coll Tianjin 300020 Peoples R China

In order to assist physicians in diagnosis and treatment planning, accurate and automatic methods of organ segmentation are needed in clinical practice. UNet and its improved models, such as UNet + + and UNt3 + , have been powerful tools for medical image segmentation. In this paper, we focus on helping the encoder extract richer features and propose a N-Net for medical image segmentation. On the basis of UNet, we propose a dual encoder model to deepen the network depth and enhance the ability of feature extraction. In our implementation, the Squeeze-and-Excitation (SE) module is added to the dual encoder model to obtain channel-level global features. In addition, the introduction of full-scale skip connections promotes the integration of low-level details and high-level semantic information. The performance of our model is tested on the lung and liver datasets, and compared with UNet, UNet + + and UNet3 + in terms of quantitative evaluation with the Dice, Recall, Precision and F1 score and qualitative evaluation. Our experiments demonstrate that N-Net outperforms the work of UNet, UNet + + and UNet3 + in these three datasets. By visual comparison of the segmentation results, N-Net produces more coherent organ boundaries and finer details.

关键词： Deep learning Image segmentation encoder-decoder Convolutional neural network

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EDSRNet: An Enhanced decoder Semantic Recovery Network for 2D Medical Image Segmentation

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2025年第2期29卷 1113-1124页

作者： Sun, Feng Zhou, Ying Hu, Longxiangfeng Li, Yongyan Zhao, Dan Chen, Yufeng He, Yu Univ Elect Sci & Technol China Mianyang Cent Hosp Sch Med Mianyang 621000 Peoples R China Mianyang Cent Hosp NHC Key Lab Nucl Technol Med Transformat Mianyang 621000 Peoples R China Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621000 Peoples R China

In recent years, with the advancement of medical imaging technology, medical image segmentation has played a key role in assisting diagnosis and treatment planning. Current deep learning-based medical image segmentation methods mainly adopt encoder-decoder architecture design and have received wide attention. However, these methods still have some limitations, including: (1) Existing methods are often influenced by the significant semantic information gap when supplementing features for the decoder. (2) Existing methods do not simultaneously consider global and local information interaction during decoding, resulting in ineffective semantic recovery. Therefore, this paper proposes a novel Enhanced decoder Semantic Recovery Network to address these challenges. Firstly, the Multi-Level Semantic Fusion (MLSF) module is introduced, which effectively fuses low-level features of the original image, encoder features, high-level features of the deepest network layer, and decoder features, and assigns weights based on semantic gaps. Secondly, the Multiscale Spatial Attention (MSSA) and Cross Convolution Channel Attention (CCCA) modules are employed to obtain richer feature information. Finally, the Global-Local Semantic Recovery (GLSR) module is designed to achieve better semantic recovery. Experiments on public datasets such as BUSI, CVC-ClinicDB, and Kvasir-SEG demonstrate that the proposed model improves IoU compared to suboptimal algorithms by 0.81%, 0.85% and 1.98%, respectively, significantly enhancing the performance of 2D medical image segmentation. This method provides effective technical support for further development in the field of medical image.

关键词： encoder-decoder multi-level semantic medical image segmentation semantic recovery encoder-decoder multi-level semantic medical image segmentation semantic recovery

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Physics descriptors enhanced Bayesian learning method for permeability of random media under sparse data

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 143卷

作者： Qi, Hang Guan, Xiaofei Chen, Qing Jiang, Zhengwu Liu, Fang Zhang, Jieqiong Zhu, Hehua Tongji Univ Sch Math Sci Minist Educ Shanghai 200092 Peoples R China Tongji Univ Key Lab Intelligent Comp & Applicat Minist Educ Shanghai 200092 Peoples R China Henan Acad Sci Inst Math Zhengzhou 450046 Peoples R China Tongji Univ Key Lab Adv Civil Engn Mat Minist Educ Shanghai 200092 Peoples R China Tongji Univ Sch Mat Sci & Engn Shanghai 200092 Peoples R China Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai 200092 Peoples R China Northwest Univ Sch Math Xian 710127 Peoples R China Tongji Univ Key Lab Geotech & Underground Engn Minist Educ Shanghai 200092 Peoples R China

Permeability is a significant property in microstructure-based material design. Currently, the main research gap in such material design can be divided into three aspects: Firstly, experimental methods face stringent conditions and poor reproducibility in accurately describing internal media structures. Secondly, machine learning methods require impractically large datasets to learn the strong nonlinear relationship between permeability and microstructure. Thirdly, there is a challenge in quantifying the inherent uncertainty of randomly distributed phases and the uncertainty propagation in the model. In this work, we propose a novel physics descriptors enhanced Bayesian learning method, aiming to predict the permeability of random media under sparse data. The key aspects of the method are the physics descriptors with clear physical indications, which characterize porous microstructure from multiple perspectives, and an encoder-decoder strategy that reduces microstructural complexity while utilizing these physics descriptors as basis functions to reconstruct permeability. The numerical experiments indicate that porosity, chord length density, and lineal-path dominate in determining permeability. Moreover, predictions with a 5%-11% error can be achieved under 32 training samples. Finally, the method provides a complete posterior distribution, where the posterior variances quantify uncertainty and benefit robust decisions in simulation-assisted materials design.

关键词： Permeability Physics descriptors Bayesian learning encoder-decoder Structure-property links Simulation-assisted materials design sparse data Uncertainty quantification

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Investigation of data-driven model predictive control for liquid nitrogen cooling on helium refrigerator

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FUSION ENGINEERING AND DESIGN 2025年 211卷

作者： Yu, Qiang Zhou, Zhiwei Yuan, Kai Li, Shanshan Zhu, Zhigang Zhuang, Ming Chinese Acad Sci Inst Plasma Phys Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

The helium refrigerator, which is a critical infrastructure of the fusion device, should be controlled well and maintain stability. During the operation of one refrigerator in the Comprehensive Research Facility for Fusion Technology, a continuous oscillation behavior was observed in the liquid nitrogen (LN2) cooling system. This paper explores a data-driven Model Predictive Control (MPC) scheme for the LN2 cooling control. Modeling the complex system dynamics under the oscillation disturbance is achieved by the encoder-decoder recurrent neural network, which provides an end-to-end implementation for multistep prediction. The data-driven MPC applies the particle swarm optimization algorithm to find the optimal control actions, in which a novelty particle initialization method is adopted to improve the search efficiency. The performance of the data-driven MPC is evaluated by closed-loop simulation, and the simulation results indicate that the disturbance can be effectively restrained. The proposed scheme shows a promising extension prospect, such as smoothing the pulse heat load disturbance in the fusion cryogenic system.

关键词： Model predictive control encoder-decoder Recurrent neural network Liquid nitrogen cooling Helium refrigerator

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ERU-Net: A novel effective 2D residual neural network for brain tumors semantic segmentation from multimodal MRI

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 101卷

作者： Mohammed, Yahya M. A. Jellouli, Ismail El Garouani, Said Abdelmalek Essaadi Univ Fac Sci Dept Comp Sci Emerging Comp Technol ECT Tetouan Morocco Sidi Mohamed Ben Abdellah Univ Fac Sci Fes Morocco

Brain tumor segmentation technology is crucial in diagnosing and treating MRI brain tumors. It aids doctors in locating and measuring tumors. It also plays a vital role in the planning and development of treatment and rehabilitation strategies. Recently, challenges have persisted despite the success achieved by deep learning- based methods in improving the segmentation accuracy of MRI brain tumor regions. These challenges are particularly prominent in small-scale tumor regions, where issues arise due to their diminutive size and the substantial variation between regions occupied by different tumor classes. Additionally, limitations in parameters and computational complexity contribute to these challenges. Thus, there remains considerable potential for enhancing these methods and increasing their efficacy. This paper presents an effective 2D residual neural network (2D ERU-Net) for segmenting MRI brain tumors. The proposed architecture consists of residual units in the encoder path to speedup training and convergence, and a deep supervision module (DSM) in the decoder path to address gradient-related issues and obtain high-resolution feature maps. Furthermore, a fusion weighted loss function, incorporating both weighted dice loss and weighted cross-entropy loss, is developed to solve the issues related to network convergence and data imbalance. The results of experiments on the reliable MRI brain tumor BraTS2019 dataset showed that the ERU-Net achieved a mean dice coefficient of 0.881, 0.882, and 0.891 and Hausdorff distances of 20.86 mm, 17.83 mm, and 13.35 mm for the tumor core, enhancing tumor, and whole tumor, respectively. Furthermore, ERU-Net demonstrates exceptional efficiency when compared to state-of-the-art methods.

关键词： 2D convolutional neural networks Automatic segmentation Brain tumor segmentation encoder-decoder Residual block Semantic segmentation Brain MRI

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MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for segmentation of polyps in colonoscopy

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 102卷

作者： Raghaw, Chandravardhan Singh Yadav, Aryan Sanjotra, Jasmer Singh Dangi, Shalini Kumar, Nagendra Indian Inst Technol IIT Indore Dept Comp Sci & Engn Indore 453552 India Indian Inst Technol IIT Indore Dept Elect Engn Indore 453552 India

Automated polyp segmentation in colonoscopy is crucial for early diagnosis and treatment planning of colorectal cancer. Early detection of polyps significantly improves survival rates. However, traditional methods rely on manual verification by endoscopic physicians, increasing the risk of missed polyps due to their variability. To address this, we propose a novel Multiscale Network with Spatial-enhanced Attention (MNetSAt) for automated polyp segmentation. MNet-SAt preserves edge information to improve polyp boundary detection and efficiently utilizes multi-scale features across spatial and channel dimensions, focusing on key regions. Additionally, a spatially enhanced attention mechanism captures spatial relationships and global context, further improving segmentation accuracy. We comprehensively evaluate the efficacy of MNet-SAt on the Kvasir-SEG and CVC-ClinicDB datasets. Qualitative and quantitative experiments demonstrate the superiority of MNet-SAt over ten state-of-the-art existing baselines and its stronger generalization performance. On Kvasir-SEG, it achieves an outstanding performance of 96.61% DSC, 96.92% IoU, 97.36% Precision, 97.83% Accuracy, and 97.12% Recall. On CVC-ClinicDB, it achieves 98.60% DSC, 95.89% IoU, 96.89% Precision, 97.32% Accuracy, and 96.13% Recall. In summary, MNet-SAt offers remarkable performance in automating polyp segmentation. The superior performance of MNet-SAt shows great potential for improving the diagnostic process in early polyp identification and more effective treatment planning, thereby contributing to lowering colorectal cancer mortality rates.

关键词： Polyp segmentation Multi-scale features Spatial attention encoder-decoder Boundary preservation

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